In the processing of images it is often necessary or desirable to provide corrections or enhancements to some characteristics of the image, by selectively modifying pixel data which represents the picture. Often it is desirable to alter the contrast of an area of interest (AOI) of the image in order to emphasize or reveal important information content therein. A number of different techniques and methodologies have been developed in the prior art for such image contrast manipulations. Some are based upon user selectable parameters, while others are automatic in their action, based upon various statistical assessments of the pixel data for an image. Digital image contrast enhancement techniques have been applied in a wide variety of fields, wherever pictures are represented by pixel data, including pictures obtained by non-visible as well as visible radiation.
In the field of radiography, for example, digital image handling systems are gaining acceptance as an alternative to, or a supplement to, conventional film-based X-ray systems. Once images have been acquired or converted to digital electronic form, they may be stored, retrieved, transmitted to remote locations, and viewed as needed on various types of output devices. Also, digital signal processing techniques may be used to enhance contrast or other properties of the image. Such images may be obtained through scanning and digitizing of conventional X-ray films. Alternatively, in digital radiography, imaging systems are provided which use photoconductive material to absorb incident radiation and form and hold a latent image of an object in the form of a distribution of charge carriers. Readout of the latent image and conversion of it to electronic form is achieved by scanning a narrow beam of radiation across the photoconductive material, and detecting the motion of charges caused thereby. Examples of this type of system are disclosed in U.S. Pat. No. 4,176,275 to Korn et al. and U.S. Pat. No. 5,268,569 to Nelson et al.
The dynamic range of images obtained through digital radiography or conventional X-ray film, i.e., the tonal range between the lightest and darkest parts of the image (Dmax and Dmin, for film) in which detail is recorded, generally exceeds the dynamic range of most output systems which might be used to view the image, such as CRT displays, film, and printers. This means that the contrast range of the image should be altered in some way in order to accommodate the output device. This may mean compressing the tonal range to fit the output medium. In other cases, it may be desirable to compress portions of the tonal range which do not contain information of interest, and expand the contrast range of the AOI to reveal the most significant information, depending upon the purpose of the image. For example, an X-ray image for viewing soft tissues would have a different contrast range of interest than one intended to look at bone structure.
In addition to matching the dynamic range of the output device, it is also important to place the tonal values of interest in a middle density range so that a trained human observer can easily see them. For example, it is easy for a human observer to see small density changes in the region of an optical density of 1.0 (medium dark), but it is very difficult to see small density changes in the region of an optical density of 3.0 (very dark), even though the detail is measurably present in both images.
Unlike X-ray film, in which only limited contrast control is available through adjustment of exposure and development parameters, a digitized image can be manipulated in a number of different ways to enhance contrast. It is possible to change the slope of the contrast curve in many ways, including different slopes in different ranges. Numeric tools are available to transform pixel values. The problem with digital image processing is not one of lacking tools with which to work on the image, but rather is one of trying to provide meaningful choices of image areas and contrast curves, out of the many possibilities.
Some systems have provided a number of user selectable modes or curves, to invoke predetermined contrast and constant algorithms. The idea would be to provide a number of programs to cover a number of commonly expected image situations, and allow the user to select. One problem with such a strategy is that the predetermined programs may not be the best for all cases. Another problem is that it requires additional steps of user time, and also possibly extra training for the user in order to know which mode is best or to interpret the results. When the same picture data set can produce a number of different-appearing displays on the same output device, depending on the selection of the mode, this can be confusing unless the user has been specifically trained in that system. This would lead to the possibility that important detail in the viewed image might be lost due to incorrect selection of the contrast mode. It is therefore desirable to have an automatic type system which can respond in some way to the data for a particular picture, to produce an optimum image, without user selection.
Also, depending upon the size of the data set for the picture and the available computation power, switching modes to compare can consume a considerable amount of time to compute and redisplay. This could be a particular problem in the case of remote systems, wherein the data transfer is slowed by communication links.
Various techniques have been proposed in the prior art for automatic picture analysis and contrast enhancement. Some examine the data array for the picture to find the low and high gray-level values and to find some predetermined scaling based on them. Others employ sophisticated statistical techniques based upon the picture dam set. A large number of these create a histogram of picture values, and then analyze it in various ways to make the contrast decision.
A histogram of the data values is a plot of the relative frequency of different image values, e.g., density values, of all the pixels in the set of pixels under consideration. Various prior art techniques then use various analyses or parameters extracted from the histogram in order to make decisions. Examples of techniques using histogram analysis are disclosed in U.S. Pat. No. 4,859,850 to Funahashi et al, U.S. Pat. No. 5,179,597 to Takeo, and U.S. Pat. No. 5,015,853 to Nakajima. One technique is to look for maxima and minima in the histogram curve. For example, in the case of a chest X-ray, there may be a high count of pixels at a very high density, corresponding to the "air" exposure of the relatively unimpeded X-rays directly to the film. There may be another relatively large count of low density pixels, corresponding to the mediastinum area. Finally, there may be another relatively large count over a broader range of intermediate to darker density areas which correspond to lung tissue. Such a histogram evidences a certain trimodal feature, and it may be possible to find the middle range value based upon this type of analysis. To the extent those techniques can successfully identify such features, they can provide a useful basis for contrast enhancement which covers a number of cases, but which falls short of optimum in many other cases.
A histogram of values is very useful in that it provides an abstraction of the content of the image, with a reduced data set, that makes computation easier, and provides a way to analyze things at a higher statistical level rather than at the pixel level. However, spatial information is lost in a histogram. In other words, the frequency of pixel gray level values is retained, but information about the spatial distribution of them is lost.